Skin Surface Reconstruction and 3D Vessels Segmentation in Speckle

Variance Optical Coherence Tomography

Marco Manfredi

, Costantino Grana

and Giovanni Pellacani

Department of Engineering“Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy

Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy

Keywords:

Medical Imaging, 3D Reconstruction, Vessel Segmentation, Visualization.

Abstract:

In this paper we present a method for in vivo surface reconstruction and 3D vessels segmentation from Speckle-

Variance Optical Coherence Tomography imaging, applied to dermatology. This novel technology allows

us to capture motion underneath the skin surface revealing the presence of blood vessels. Standard OCT

visualization techniques are inappropriate for this new source of information, that is crucial in early skin

cancer diagnosis. We investigate 3D reconstruction techniques for better visualization of both the external and

internal structure of skin lesions, as a tool to help clinicians in the task of qualitative tumor evaluation.

1 INTRODUCTION

Optical Coherence Tomography (OCT) is a laser-

based imaging modality that is capable of provid-

ing detailed pictures of sub-surface tissue microstruc-

ture to a depth of 1 mm+ in real time, and has been

very successfully applied to imaging the eye (Huang

et al., 1991; Leitgeb et al., 2003). OCT was ﬁrst ap-

plied to imaging skin as early as 1997 (Welzel et al.,

1997). However, image quality and scanning speed

remained insufﬁcient until multi-beam OCT was de-

veloped (Holmes et al., 2008). Multi-beam OCT com-

bines OCT data from multiple laser beams focused at

slightly different depths in the tissue, scanned simul-

taneously through the region of interest.

“Standard” OCT relies on the small variations in

intensity of back-scattered light from different tissue

cellular microstructures to reveal the tissue morphol-

ogy (Huang et al., 1991). The resulting contrast be-

tween some tissue types can be quite low, and so there

has been continued interest in development of tech-

niques with the potential of extracting further clini-

cally useful information from the OCT data (Schmitz

et al., 2013; Hitzenberger and Fercher, 1999).

One technique of great promise is speckle-

variance OCT (SV-OCT) (Mariampillai et al., 2008;

Mahmud et al., 2013; De Carvalho et al., 2015).

Brieﬂy, SV-OCT involves rapidly repeating OCT

scans, and analyzing the statistics of the OCT sig-

nal, to detect regions of the OCT images which have

changed between these successive scans. Most of the

Figure 1: Sample slices from a Dynamic OCT scan. The

redness information highlights motion underneath the skin

surface, and it is used to detect blood vessels.

scanned tissue is unchanged, but any blood ﬂowing

produces small changes in the SV-OCT data that are

detectable, thereby revealing the presence of blood

vessels and capillaries. SV-OCT allows the visu-

alization of vessels as small as ~20µm in diameter

without the use of contrast agents. The analysis of

vascular networks is fundamental because tumors re-

quire access to blood vessels for oxygen and nutrients,

producing abnormalities in vessel morphology (Jain,

2001).

In our images, the areas of motion are highlighted

in red, with the most intense red corresponding to the

234

Manfredi, M., Grana, C. and Pellacani, G.

Skin Surface Reconstruction and 3D Vessels Segmentation in Speckle Variance Optical Coherence Tomography.

DOI: 10.5220/0005758702340240

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, pages 234-240

ISBN: 978-989-758-175-5

strongest motion. See Figure 1 for examples. We use

the term “dynamic OCT” to distinguish the detected

motion from the standard OCT image data, which we

refer to as “structural OCT”.

Structural OCT visualization software is inca-

pable of showing the vascular structures in an effec-

tive way, since they are built to work on structural

OCT data. The potential informativeness of blood

vessel detection has to be investigated using new vi-

sualization methods.

This paper focuses on the visualization of dy-

namic OCT images by means of 3D reconstruction

algorithms to capture external and internal structures

of skin lesions. The novelty of the work consists

in proposing an image analysis pipeline to deal with

novel dynamic OCT data, with the goal of presenting

it at best for qualitative in vivo tumor diagnosis.

2 RELATED WORK

In the following we will present notable works on the

topics of OCT image analysis and vessel segmenta-

tion both in 2D and 3D.

A lot of work in recent years has been done in reti-

nal vessel segmentation (Soares et al., 2006; Niemei-

jer et al., 2008). In (Soares et al., 2006) multi-scale

Morlet Wavelet transform responses are used as fea-

tures in a binary classiﬁcation approach to highlight

the presence of vessels of different diameters. 3D

OCT volumes are used in (Niemeijer et al., 2008)

to obtain a 2D projection image from a segmented

layer for which the contrast between the vascular sil-

houettes and background is highest. The high con-

trast between foreground and background allows their

method to effectively segment the vessel silhouettes

using a supervised pixel-based approach. Differently

from retinal OCT images, the vascular network of

skin lesions has a 3D structure that can not be cap-

tured by a single 2D view. We have to tackle 3D

vascular segmentation. 3D vascular segmentation and

reconstruction have been applied for years to brain

imaging (Wu et al., 2011; Manniesing et al., 2006;

Lesage et al., 2009; Tankyevych et al., 2009). In (Wu

et al., 2011) images are cleaned with an anisotropic

ﬁltering, followed by skin and bones removal. Level

sets are then applied for the segmentation of the

vascular network. Their work then focuses on re-

construction of the segmented vessels demonstrating

the effectiveness of an ellipse model assumption and

proposing a solution to the branching problem. A no-

table difference between Computed Tomography An-

giography or Magnetic Resonance Angiography im-

ages used in these works and OCT imaging, is that

Input image

Convolution

Maximum Response

After non-maxima suppression

Figure 2: The ﬁltering procedure. Circular kernels are ap-

plied to highlight the surface boundary followed by a non-

maxima suppression step to obtain a single response per

edge.

OCT has a limited penetration depth (about 1mm).

As can be seen in Figure 1, only superﬁcial structures

are detected, leading to potentially incomplete vascu-

lar networks. Furthermore, the amount of noise in

dynamic OCT images increases when moving deeper

in the tissue. A straight-forward application of the

methods listed above is not possible for dynamic OCT

imaging and alternative solutions must be explored.

A preliminary work on SV-OCT, proposed

in (Conroy et al., 2012), tackles the problem of quan-

titative measurements of vascular structures. They

propose to measure quantitative metrics to charac-

terize the vessel morphology, and they validate their

approach on mice (comparing healthy and tumor-

bearing mice). Our work differs from theirs as: (i)

the subjects of our analysis are humans and not mice,

(ii) they minimize bulk motions with a customized

holder, while our images are captured with a hand-

held device (resulting in higher levels of noise) and

(iii) we detail the image analysis pipeline to achieve

clear visualization of the vascular networks and for

the reconstruction of the lesion surface.

3 SURFACE RECONSTRUCTION

This section details the algorithm for the reconstruc-

tion of the top surface of the skin. Having the 3D

shape of the surface allows a better visualization of

the vascular networks that can be analyzed at uniform

depths from the outer surface, as shown in Section 4.

Horizontal slices of the epidermis can thus follow the

shape of the interested region helping the clinicians in

the diagnosis. The input data are 121 RGB images of

size 1370×460 coming from the OCT scanner, rep-

resenting vertical slices of skin, covering an area of

approximate size of 5mm×5mm. The expected out-

Skin Surface Reconstruction and 3D Vessels Segmentation in Speckle Variance Optical Coherence Tomography

235

100

150

200

250

300

350

400

450

500

0 200 400 600 800 1000 1200

Height of the surface

Spline

Data points

Subsampling

Figure 3: Reconstructing a smooth surface one slice at a time: the original data points (red) are subsampled (black) and after

a median ﬁlter they are used as reference points for a second-order spline curve ﬁtting (cyan).

put is an image of size 1370×121 where each element

contains the height of the reconstructed surface. Since

the scanned area of skin is squared, a ﬁnal resizing

step to 1370×1370 is applied to compensate the un-

even horizontal/vertical resolution of the scanner.

The challenges of this task are represented by the

size of the input data (about 220MB per scan) and by

the noise of the input images. Since the scanner is

hand-held by the clinician, misalignments are often

present between adjacent slices. Some slices from a

scan are presented in Figure 1.

To reconstruct the surface, gray level images are

used since color information (i.e. redness) only deﬁne

the vascular structure underneath the epidermis.

Our approach starts by analyzing each slice sepa-

rately, ﬁnding a rough estimation of the height of the

surface. Inspired by (Arbelaez et al., 2011), that suc-

cessfully proposed a method for contour detection in

natural images, given a slice a collection of N circular

ﬁlters at different orientations is applied to highlight

the boundary of the skin.

A ﬁxed size of 25×25 is chosen for all kernels,

which has been proven to be robust to the noise level

of the input data. The N convolutions are reduced to

one image where each pixel is the maximum response

over all ﬁlters. The higher the number of ﬁlters N,

the more accurate the result is because the orientation

quantization is lower. However, we found that in prac-

tice using 3 ﬁlters produces an accurate response for

the surface. The orientations of the 3 ﬁlters are -45°,

0° and 45°. The ﬁltering step produces a smooth re-

sponse over the skin boundary, where we would like

to have a thin line following the surface; non-maxima

suppression is thus applied (Canny, 1986). The ﬁlter-

ing pipeline is summarized in Figure 2.

To obtain a unique response for each column of

the slice, we select the topmost pixel the has a re-

sponse greater than a threshold T . The choice of T has

been done experimentally (T = 0.1 where the maxi-

mum response is 1.0) but as it can be seen in the last

picture of Figure 2, the very high contrast between the

boundary and the rest of the image makes the choice

of T quite robust to noise.

A tradeoff between reconstruction accuracy and

smoothness is unavoidable. A very accurate recon-

struction would maintain every small curvature of the

skin, at the cost of losing some robustness to noise

where hair or other artifacts are present. Smoothing

the surface too much could create discrepancies be-

tween the actual surface and the reconstruction, that

we would like to avoid. Since the clinicians do not

need a ﬁne detail reconstruction, we decided to do a

subsampling of the available data points (found after

the threshold). Taking less points does not prevent

noisy data to be selected and for this reason we apply

a median ﬁlter along the line. This step allows to re-

move noisy peaks and drops. Finally, a second-order

spline curve ﬁtting is applied to smoothly connect the

available reference points. The subsampling step has

been set to 15, the length of the median ﬁlter to 5. In

Figure 3 the same slice of Figure 2 is analyzed using

the described algorithm.

At this point we have independently reconstructed

121 curves (one per slice), what we need to do is

to enforce smoothness along the perpendicular direc-

tion to the slices. The same pipeline of algorithms

is applied vertically: (i) subsampling (step=3 in this

case) (ii) median ﬁlter with size 5 to remove drops

and peaks and (iii) spline interpolation. The only dif-

ference here is that the surface must be stretched to

squared proportions for the ﬁnal result. In Figure 4 the

contributions of the slice-wise and vertical smoothing

are highlighted. Most of the unwanted artifacts are re-

moved by the ﬁrst step, but the vertical smoothing is

still required to remove bigger discontinuities and to

smoothly connect adjacent slices.

The ﬁltering and the spline ﬁtting steps, that are

the most computationally expensive, have been par-

allelized (since each slice is analyzed independently).

The running time of the complete pipeline on a mod-

ern pc (Intel-i7 4790k) is of 3-4 seconds.

Once the shape of the surface has been recon-

structed, textures are applied on it using the input

data. We can thus obtain a 3D visualization of the

surface, presented in Figure 5.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

236

Figure 4: Top view of the reconstructed surface, viewed in the original dimensions of 1370×121 (best viewed in color). The

color of the surface encodes the depth (blue=shallow, red=deep). In a) the surface after the ﬁltering step is presented; drops

and peaks are evident. b) is the result after the horizontal subsampling and spline ﬁtting. Some artifacts are still visible (e.g.

areas 1, 2 and 3) and are further corrected with the vertical smoothing step shown in c).

Figure 5: 3D visualization of the reconstructed surface from multiple views.

4 VESSELS VISUALIZATION

The novelty of the dynamic OCT over standard OCT

technologies resides in capturing motions underneath

the surface of the skin. This motion carries informa-

tion about blood ﬂows, giving insights about the vas-

cular network. The visualization of the vascular net-

work is not trivial and we identiﬁed 3 ways of pre-

senting it to the clinician:

1. Horizontal view: vertical slices can be used to cre-

ate a top-view by sampling horizontal images that

better present the vascular network compared to

the partial information of each vertical slice sepa-

rately.

2. Depth preserving horizontal view: the horizontal

view can be enhanced by using the reconstructed

3D surface to sample data at uniform depths. We

thus obtain a horizontal view in which every point

is captured at the same depth from the surface.

The advantage of this solution compared to the

previous one becomes clear when analyzing par-

ticularly convex skin lesions.

3. 3D view: the most informative way of visualizing

the vascular network consist of 3D visualization.

Several problems have to be tackled to obtain a

realistic and clear view of the vessels, like the

removal of all non-vessel data. In the following

section we will detail the proposed procedure.

In Figure 6 a comparison of the three approaches

discussed above clearly highlights the improvements

in informativeness of the depth preserving horizontal

view and 3D view over the standard approach. The

convexity of the analyzed lesion leads the horizontal

view to fail in capturing the global vessel structure.

Although the depth preserving horizontal view solves

this problem, it still shows one layer at a time, while

the 3D view presents the complete vessel network.

4.1 3D Vessel Segmentation

Blood ﬂow information is encoded as redness in the

dynamic OCT images, since only the detected motion

is colored while the rest of the image is gray-scale.

As can be seen in Figure 1, the red channel tends to

saturate in the presence of strong motion, while lower

values of the red channel are often caused by noise.

Since the OCT scanner is hand-held, any small shift

of the device or of the captured skin lesion is cap-

tured as a motion and reproduced as redness in the

corresponding areas. In the middle image of Figure 1,

red peaks caused by unintended motions are visible

on the left. As a consequence we have to design our

vessel reconstruction algorithm to cope with noisy de-

tections.

To remove all the data except the vessel network

we apply a threshold on the difference between the

red channel and the blue channel for each pixel in-

Skin Surface Reconstruction and 3D Vessels Segmentation in Speckle Variance Optical Coherence Tomography

237

Horizontal view

Depth preserving

horizontal view

3D view

Figure 6: A comparison of three different solutions to present the vascular network. The surface extraction method, presented

in Section 3, allows to extract horizontal layers at constant depth from the surface, as shown in the middle images. On the

right, the 3D view detailed in Section 4.1 is reported.

dependently. The threshold is set empirically to 180

using tens of scans as reference.

On a regular scan, the point cloud that we ob-

tain is composed of tens of millions of points (the

scan of Figure 6 produces approximately 25 million

of points). To speed-up the reconstruction we sub-

sample the points using a step of 2 in each direction,

thus lowering the number of points to 3 millions. The

top-left picture of Figure 7 shows the resulting point

cloud from a top-view. Although the vascular struc-

ture is partially visible, the amount of noise is very

high.

3D Point Cloud Filtering

We design the ﬁltering step as follows:

1. A 3D ﬁlter is computed using a cubic kernel cen-

tered at each point location, with side s1. The ra-

tio P

of red points inside the cube divided by its

volume is computed and only the red points with

≥ 50% are kept. This step removes isolated

points, while maintaining dense structures typical

of blood vessels.

2. Since the visualization of a blood vessel only re-

quires the rendering of the outer surface, all the

data points that lies in the inner part of a ves-

sel “tube” are in fact useless. Moreover, they

deeply impact the computational performance of

the mesh reconstruction algorithm. We thus re-

move all the data points whose neighbors (26

in total) entirely belong to the point cloud. For

this purpose, another cubic ﬁlter with side s2 = 1

(s2 < s1) is used.

Applying the previously described ﬁlters requires

the computation of the number of red points in cu-

bic boxes centered in each location of the point cloud

(we will refer to this number ad the mass of a box).

The size of the point cloud is easily computed from

the size of the scan images as 1370 × 460 × 1370 =

863374000 points. A trivial solution that iterates

through each point is unfeasible and leads to several

minutes of computation.

In the “integral images” approach, introduced in

(Viola and Jones, 2004), the integral image contains

at every point (x

, y

) the sum of all pixels with x < x

and y < y

. This allows to compute the sum of all val-

ues of a rectangle by combining just four values of the

integral image. In this paper we use an extension to

3D volumes (Grana et al., 2012) to compute in con-

stant time the mass of a box. To this aim the ﬁrst step

is to extract the 3D integral point cloud.

Having p(c), the value of the point cloud at c

(p(c) = 1 or p(c) = 0), the 3D integral point cloud

ip(c) is deﬁned as

ip(c) =

∑

x:x

k=0,1,2

p(x) (1)

As for integral images, it is possible to compute the

3D integral point cloud by a single sweep over the

original data points, taking advance of the recursive

nature of the deﬁnition. In particular:

ip(c) = p(c) + ip(c

− 1, c

, c

) + ip(c

, c

− 1, c

)

− ip(c

− 1, c

) + ip(c

, c

− 1)

− ip(c

− 1, c

, c

− 1) − ip(c

, c

− 1, c

− 1)

+ ip(c

− 1, c

− 1)

(2)

This assumes that the value of the 3D integral point

cloud is 0 whenever any of the coordinates is negative.

Given a 3D integral point cloud, the computation of

the mass within a box b = (l, h) is quite similar to the

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

238

After subsampling

3.18 Millions Points

After filtering

1.04 Millions Points

After Delaunay Triangulation

After Laplacian Smoothing

Figure 7: From a point-cloud to a smooth 3D mesh: the

original (subsampled) point-cloud (top-left), the result after

ﬁltering (top-right), the mesh generated using Delaunay tri-

angulation (bottom-left) and the ﬁnal result (bottom-right).

All images are captured from a top-view camera, while in

the proposed visualization tool full 3D object manipulation

can be performed.

use of integral images for rectangle area calculations:

m(b) = ip(h

, h

) − ip(l

− 1, h

, h

)

− ip(h

, l

− 1, h

) + ip(l

− 1, l

− 1, h

)

− ip(h

, h

, l

− 1) + ip(l

− 1, h

, l

− 1)

+ ip(h

, l

− 1, l

− 1) − ip(l

− 1, l

− 1)

(3)

This formulation holds, again assuming that ip(c) = 0

if c

= −1 for any k. The output of the ﬁltering step

is shown in the top-right picture of Figure 7.

Mesh Reconstruction

The output of the ﬁltering step is a cleaned 3D point

cloud with hollowed tubes representing blood vessels.

A standard solution when dealing with mesh recon-

struction from point cloud is the use of surface recon-

struction algorithms based on point normals, like the

algorithm proposed in (Kazhdan et al., 2006). How-

ever, the proximity between adjacent vessels and the

presence of holes/peaks complicates the computation

of reliable point normals.

For this reason we choose the Delaunay triangu-

lation, a technique that does not require any a pri-

ori knowledge of surface orientation normals. The

only parameter of the Delaunay triangulation, α, is

the maximum radius length used when triangulating

neighboring points. A too high value of α could

merge adjacent vessels (if the mutual distance be-

tween the two is comparable to α), a too low value

of α could create artifacts in the reconstruction and,

more speciﬁcally, holes. We set α so to obtain the

best trade-off between regularity of the vessels struc-

tures and the ﬁne details representation. The result of

Delaunay triangulation is presented in the bottom-left

picture of Figure 7.

The visualization of the vascular network thus

obtained can be further improved with a Laplacian

smoothing step (Field, 1988), giving to the clinician a

more realistic view of the vessels appearance. The

ﬁnal view presented to the user is presented in the

bottom-right picture of Figure 7.

5 CONCLUSION

In this paper we tackled the problem of visualization

of 3D surfaces and vascular networks from dynamic

OCT in vivo scans. This recently introduced tech-

nique for OCT imaging poses new challenges to the

image analysis community, that cannot be addressed

with straightforward application of previously pro-

posed methods. Our aim was to provide to the clini-

cian a tool for qualitative skin lesions evaluation. We

detailed a complete pipeline to deal with image noise,

surface reconstruction, vessel segmentation and 3D

visualization.

ACKNOWLEDGEMENT

The research leading to these results has received

funding from the European Union’s Competitiveness

and Innovation framework Programme CIP 2007-

2013. Project Name: ADVANCE, reference: 621015.

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